Digital Transp. Syst., San Diego, CA.
IEEE Trans Image Process. 1997;6(3):383-97. doi: 10.1109/83.557341.
Two-dimensional (2-D) adaptive filtering is a technique that can be applied to many image processing applications. This paper will focus on the development of an improved 2-D adaptive lattice algorithm (2-D AL) and its application to the removal of correlated clutter to enhance the detectability of small objects in images. The two improvements proposed here are increased flexibility in the calculation of the reflection coefficients and a 2-D method to update the correlations used in the 2-D AL algorithm. The 2-D AL algorithm is shown to predict correlated clutter in image data and the resulting filter is compared with an ideal Wiener-Hopf filter. The results of the clutter removal will be compared to previously published ones for a 2-D least mean square (LMS) algorithm. 2-D AL is better able to predict spatially varying clutter than the 2-D LMS algorithm, since it converges faster to new image properties. Examples of these improvements are shown for a spatially varying 2-D sinusoid in white noise and simulated clouds. The 2-D LMS and 2-D AL algorithms are also shown to enhance a mammogram image for the detection of small microcalcifications and stellate lesions.
二维（2-D）自适应滤波是一种可应用于许多图像处理应用的技术。本文将重点介绍改进的二维自适应格型算法（2-D AL）的开发及其在去除相关杂波以提高图像中小目标可检测性中的应用。这里提出的两个改进是增加了反射系数计算的灵活性和一种用于更新 2-D AL 算法中使用的相关性的 2-D 方法。该 2-D AL 算法被证明可以预测图像数据中的相关杂波，并且所得到的滤波器与理想 Wiener-Hopf 滤波器进行了比较。将去除杂波的结果与之前发表的二维最小均方（LMS）算法的结果进行了比较。2-D AL 比 2-D LMS 算法更能预测空间变化的杂波，因为它能够更快地收敛到新的图像特性。对于白色噪声中的空间变化的二维正弦波和模拟云，展示了这些改进的示例。还展示了 2-D LMS 和 2-D AL 算法如何增强乳房 X 光照片以检测小的微钙化和星状病变。
